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ObjectiveTo explore the content difference of gallic acid，protocatechuic acid，catechin，total flavonoids， and total polysaccharides in Cynomorii Herba between different producing areas and the correlation between effective components and environmental factors. MethodNinety-five batches of Cynomorii Herba samples were collected from 12 major producing areas in five provinces （autonomous regions）， including Gansu，Inner Mongolia，Xinjiang，Qinghai， and Ningxia，and the geographical-climatic factors such as altitude，longitude and latitude，climate type，annual average frost-free period，annual rainfall，annual sunshine hours，annual average temperature， and annual average evaporation were recorded. The content of gallic acid，protocatechuic acid， and catechin in Cynomorii Herba was determined by high-performance liquid chromatography （HPLC），and the mathematical model of the correlation between the content of chemical components and environmental factors was optimized and established. ResultPearson correlation analysis showed that protocatechuic acid content in Cynomorii Herba increased with the increase in annual average frost-free period and annual average temperature， and catechin content increased with the increase in the annual average frost-free period，annual sunshine hours， and annual average evaporation，while total polysaccharides content decreased with the increase in altitude. Redundancy analysis （RDA） showed that the annual average frost-free period， annual average evaporation，annual sunshine hours， and altitude had great influences on the content of effective components in Cynomorii Herba. Curve fitting showed that the optimal conditions for the growth of Cynomorii Herba were as follows： altitude of 800-2 000 m，annual average frost-free period of 80-110 d， annual rainfall of 110-300 mm，annual sunshine hours of 2 400-3 000 h， annual average temperature of 2.2-8.8 ℃，and annual average evaporation of 1 700-2 500 mm. ConclusionThe content of effective components in Cynomorii Herba is diverse in terms of producing areas and shows a clear response rule to environmental factors. The areas suitable for growing and artificial cultivation introduction are those with high altitude，short annual average frost-free period，low annual rainfall，large average evaporation，long sunshine hours， and low annual average temperature.
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Objective::To establish a pre-column derivatization reverse-phase high-performance liquid chromatography (RP-HPLC) method for the simultaneous determination of 17 amino acids in Cynomorii Herba from different producing areas and conduct a multivariate statistical analysis. Method::RP-HPLC with pre-column derivatization was employed, with phenyl isothiocyanate (PITC) as derivatization reagent. Separation was performed on a WondaSil C18-WR column (4.6 mm×150 mm, 5 μm), with 0.05 mol·L-1 sodium acetate solution (pH 6.5) as mobile phase A, and acetonitrile-methanol-water (3∶1∶1) as mobile phase B for gradient elution at a flow rate of 0.8 mL·min-1. The detective wave length was set at 254 nm, and the column temperature was maintained at 35 ℃. Principal component analysis (PCA) and systematic cluster analysis (HCA) models were established for multivariate statistical analysis and quality evaluation. Result::17 Kinds of amino acid were detected in Cynomorii Herba, 7 of which were essential amino acids. The 17 amino acids showed good linearity in respective concentration range, r = 0.999 0-0.999 9.The average recoveries were between 98.03%-103.89%with RSD<3.5%. The results of PCA and HCA were basically the same, and both methods can be used to clearly distinguish Cynomorii Herba from 12 municipal producing areas into 6 regions. PCA can be used to classify Cynomorii Herba according to different municipal or provincial production areas, and HCA can be used to classify it according to provincial production areas. It showed that the amino acid contents in Cynomorii Herba from different municipal and provincial producing areas had differences, and the content distribution showed obvious geographical clustering characteristics. PCA showed that Cynomorii Herba from Gansu province and Inner Mongolia had higher amino acid contents and better quality as compared with other producing areas. Conclusion::The established method can be used for content determination of 17 amino acids in Cynomorii Herba from different producing areas, and provide a reference for its comprehensive quality evaluation.
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Objective: To realize the classification and identification of Cynomorii Herba from different producing areas based on fourier transform infrared spectroscopy (FTIR) and chemometrics. Method: FTIR spectrum data of 106 batches of Cynomorii Herba from 12 cities in 5 provinces were collected by transmission method and preprocessed. The FTIR fingerprints of Cynomorii Herba were established, and spectrum analysis was performed. The FTIR similarities of Cynomorii Herba from different producing areas were calculated by correlation coefficient method. The first derivative (1D) spectrum of average FTIR of Cynomorii Herba from different producing areas were obtained. The soft independent modeling of class analog (SIMCA) model based on principal component analysis (PCA) was established by the preprocessed 1D spectrum data. The orthogonal partial least squares (OPLS) model was established by top 6 principal components. Result: The FTIR fingerprint trend and main absorption peaks of Cynomorii Herba from different producing areas were basically the same,and 16 common characteristic absorption peaks were recognized. Similarity and 1D spectrum of FTIR fingerprint of Cynomorii Herba from different producing areas showed significant and unique characteristics. The established SIMCA model can realize the classification and identification of Cynomorii Herba from different provinces,while OPLS model can realize accurate classification and identification of Cynomorii Herba in different cities. The classification and identification of Cynomorii Herba from 12 city producing areas showed obvious geographical clustering characteristics. Conclusion: The established method based on FTIR and chemometrics can realize the classification and identification of Cynomorii Herba from 12 cities.
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Objective: To optimize the extraction processes used for enzymatic hydrolysis of total sugar from Cynomorii Herba (Cynomoriumsongaricum) by employed quadratic orthogonal rotation regression combination design. Methods: Taking the extraction rate of total sugar as index, as well as enzyme ratio, enzyme concentration, extraction temperature, extraction time, and initial pH value as investigation factors to screen out main factors. The quadratic orthogonal rotation regression combination design was applied to optimize the process. And then, to employ response surface analysis method (RSM) for evaluating the impacts on the response values of the single factor effect and interaction effect of each factor level. Results: The optimum conditions were as follows: enzyme concentration was 1% (wt% of Cynomorii Herba powder), initial enzyme solution pH was 5.5, extraction temperature was 55℃, extraction time was 45 min, and pectinase and cellulose was 1∶1 proportion. The extraction yield of total sugar was (17.54 ± 0.35) %. The influence of response values of each factor presented the relationship of the parabolic surface with the opening downward. The results showed that the impact degree on total sugar yield of Cynomorii Herba were extraction temperature, extraction time, and enzyme ratio, respectively. Conclusion: This method is a simple and effective method to increase the extraction yield of total sugar from Cynomorii Herba. Quadratic orthogonal rotation regression combination design has a favourable predictability.